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draft_reply

Generate a suggested reply to a guest message grounded in the listing's fields, house rules, amenities, and calendar prices. Read-only; it never sends a message.

Instructions

Suggested reply grounded in THAT listing's fields, house rules, amenityId values, and calendar prices. Never invent rates (use calendar or say unknown). Never include wifi or door codes. Never POSTs a message — send is always false and there is no send endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionNoOptional focus question; defaults to the latest incoming message
conversationIdYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full behavioral burden and does so excellently. It discloses that the reply is grounded in specific data, must never invent rates, must omit wifi/door codes, and never sends because send is always false and there is no send endpoint. This is strong, non-redundant behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with zero filler. The core purpose is front-loaded, followed by three critical constraints, each sentence earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's behavior and constraints thoroughly, but with no output schema it does not clarify the exact return shape beyond 'suggested reply'. It also assumes the agent understands 'THAT listing's' without explicit context, though this is a minor gap given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50% because conversationId has no description, and the tool description does not compensate by explaining what conversationId identifies or how it relates to 'THAT listing's'. The 'question' parameter is adequately documented, but the required parameter remains semantically unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly indicates the tool produces a suggested reply grounded in listing fields, house rules, amenityId values, and calendar prices, distinguishing it from the list/report/get sibling tools. The verb 'draft' is implicit via the name and the output type, so purpose is identifiable but slightly indirect.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives, but the sibling set makes the purpose clear by exclusion. It does provide operational boundaries (never send, never invent rates), which indirectly guide usage, but explicit when-to-use guidance is missing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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